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import torch
import gradio as gr
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
MODEL_NAME = "openai/whisper-large-v3"
BATCH_SIZE = 8
FILE_LIMIT_MB = 1000
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
def transcribe(inputs, task):
if inputs is None:
raise gr.Error("No audio file submitted! Please upload an audio file before submitting your request.")
text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
return text
demo = gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"),
gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
],
outputs="text",
layout="horizontal",
theme="huggingface",
title="Whisper Large V3: Transcribe Audio",
description=(
"Transcribe audio files with the click of a button! This demo uses the OpenAI Whisper"
f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
" of arbitrary length."
),
allow_flagging="never",
)
demo.launch(enable_queue=True) |